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However, identifying these conditions in their early stages remains a significant challenge. Our goal was to develop a method capable of detecting latent disease liability in healthy volunteers. Methods: Using questionnaires examining affective temperament and schizotypal traits among voluntary, healthy university students (N = 710), we created three groups. These were a group characterized by an emphasis on positive schizotypal traits (N = 20), a group showing cyclothymic temperament traits (N = 17), and a control group showing no susceptibility in either direction (N = 21). We performed a resting-state EEG examination as part of a complex psychological, electrophysiological, psychophysiological, and laboratory battery, and we developed feature-selection machine-learning methods to differentiate the low-risk groups. Results: Both low-risk groups could be reliably (with 90% accuracy) separated from the control group. Conclusions: Models applied to the data allowed us to differentiate between healthy university students with latent schizotypal or bipolar tendencies. Our research may improve the sensitivity and specificity of risk-state identification, leading to more effective and safer secondary prevention strategies for individuals in the prodromal phases of these disorders.<\/jats:p>","DOI":"10.3390\/diagnostics15040454","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T05:10:22Z","timestamp":1739423422000},"page":"454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3318-1114","authenticated-orcid":false,"given":"Fl\u00f3ri\u00e1n","family":"Gubics","sequence":"first","affiliation":[{"name":"Department of Medical Genetics, Doctoral School of Interdisciplinary Medicine, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3321-004X","authenticated-orcid":false,"given":"\u00c1d\u00e1m","family":"Nagy","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9459-912X","authenticated-orcid":false,"given":"J\u00f3zsef","family":"Dombi","sequence":"additional","affiliation":[{"name":"Department of Computer Algorithms and Artificial Intelligence, University of Szeged, \u00c1rp\u00e1d Square 2, 6720 Szeged, Hungary"},{"name":"HUN-REN-SZTE Research Group on Artificial Intelligence, Institute of Informatics, University of Szeged, Tisza Lajos Boulevard 103, 6725 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ant\u00f3nia","family":"P\u00e1lfi","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3863-7595","authenticated-orcid":false,"given":"Zolt\u00e1n","family":"Szab\u00f3","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9561-6857","authenticated-orcid":false,"given":"Zsolt J\u00e1nos","family":"Viharos","sequence":"additional","affiliation":[{"name":"HUN-REN Institute for Computer Science and Control (SZTAKI), Center of Excellence in Production Informatics and Control, Centre of Excellence of the Hungarian Academy of Sciences (MTA), Kende Street 13-17, H-1111 Budapest, Hungary"},{"name":"Faculty of Economics and Business, John von Neumann University, Izs\u00e1k Street 10, 6400 Kecskem\u00e9t, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4316-0373","authenticated-orcid":false,"given":"Anh Tuan","family":"Hoang","sequence":"additional","affiliation":[{"name":"HUN-REN Institute for Computer Science and Control (SZTAKI), Center of Excellence in Production Informatics and Control, Centre of Excellence of the Hungarian Academy of Sciences (MTA), Kende Street 13-17, H-1111 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7793-2661","authenticated-orcid":false,"given":"Vilmos","family":"Bilicki","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2612-7291","authenticated-orcid":false,"given":"Istv\u00e1n","family":"Szendi","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Kiskunhalas Semmelweis Hospital, Dr. Monszpart L\u00e1szl\u00f3 Street 1, 6400 Kiskunhalas, Hungary"},{"name":"Department of Clinical- and Health Psychology, Institute of Psychology, University of Szeged, Egyetem Street 2, 6720 Szeged, Hungary"},{"name":"Centre of Excellence for Interdisciplinary Research, Development and Innovation, University of Szeged, Dugonics Square 13, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"McGrath, J., Saha, S., Welham, J., El Saadi, O., MacCauley, C., and Chant, D. (2004). A systematic review of the incidence of schizophrenia: The distribution of rates and the influence of sex, urbanicity, migrant status and methodology. BMC Med., 2.","DOI":"10.1186\/1741-7015-2-13"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1001\/archpsyc.64.5.543","article-title":"Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication","volume":"65","author":"Merikangas","year":"2007","journal-title":"Arch. Gen. Psychiatry"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e91","DOI":"10.1017\/S2045796019000891","article-title":"Trends in the incidence and DALYs of schizophrenia at the global, regional and national levels: Results from the Global Burden of Disease Study 2017","volume":"29","author":"He","year":"2020","journal-title":"Epidemiol. Psychiatr. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.jpsychires.2020.03.015","article-title":"Trends in the incidence and DALYs of bipolar disorder at global, regional, and national levels: Results from the global burden of Disease Study 2017","volume":"125","author":"He","year":"2020","journal-title":"J. Psychiatr. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11126-008-9076-0","article-title":"The diagnosis and treatment of individuals with mental illness and developmental disabilities: An overview","volume":"79","author":"Barnhill","year":"2008","journal-title":"Psychiatr. Q."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Colombo, F., Calesella, F., Mazza, m.G., Melloni, E.m.T., Morelli, m.J., Scotti, G.m., Benedetti, F., Bollettini, I., and Vai, B. (2022). Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis. Neurosci. Biobehav. Rev., 135.","DOI":"10.1016\/j.neubiorev.2022.104552"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rahul, J., Sharma, D., Sharma, L.D., Nanda, U., and Sarkar, A.K. (2024). A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. Front. Hum. Neurosci., 18.","DOI":"10.3389\/fnhum.2024.1347082"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1038\/mp.2014.139","article-title":"Towards a classification of biomarkers of neuropsychiatric disease: From encompass to compass","volume":"20","author":"Davis","year":"2015","journal-title":"Mol. Psychiatry"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1017\/S0033291714001512","article-title":"Do premorbid and post-onset cognitive functioning differ between schizophrenia and bipolar disorder? A systematic review and meta-analysis","volume":"45","author":"Trotta","year":"2015","journal-title":"Psychol. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1176\/appi.ajp.2016.15070890","article-title":"An individualized risk calculator for research in prodromal psychosis","volume":"173","author":"Cannon","year":"2016","journal-title":"Am. J. Psychiatry"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Newson, J.J., and Thiagarajan, T.C. (2019). EEG frequency bands in psychiatric disorders: A review of resting state studies. Front. Hum. Neurosci., 12.","DOI":"10.3389\/fnhum.2018.00521"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102617","DOI":"10.1016\/j.nicl.2021.102617","article-title":"Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine learning approach","volume":"30","author":"Sargent","year":"2021","journal-title":"NeuroImage Clin."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1109\/TBME.2014.2317881","article-title":"Human Brain Distinctiveness Based on EEG Spectral Coherence Connectivity","volume":"61","author":"Rocca","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","first-page":"1","article-title":"EEG Variabilities in Diagnosis of Schizophrenia, Bipolar Disorder, and PTSD: A Literature Review","volume":"1","author":"Solomon","year":"2023","journal-title":"J. Neurophysiol. Monit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.clinph.2019.06.234","article-title":"International Federation of Clinical Neurophysiology (IFCN)\u2013EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies","volume":"131","author":"Babiloni","year":"2020","journal-title":"Clin. Neurophysiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Perrottelli, A., Giordano, G.m., Brando, F., Giuliani, L., and Mucci, A. (2021). EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review. Front. Psychiatry, 12.","DOI":"10.3389\/fpsyt.2021.653642"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1006\/nimg.2002.1070","article-title":"Millisecond by millisecond, year by year: Normative EEG microstates and developmental stages","volume":"16","author":"Koenig","year":"2002","journal-title":"Neuroimage"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.neuroimage.2017.11.062","article-title":"EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review","volume":"180","author":"Michel","year":"2018","journal-title":"Neuroimage"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112938","DOI":"10.1016\/j.psychres.2020.112938","article-title":"Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls","volume":"288","author":"Baradits","year":"2020","journal-title":"Psychiatry Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, F., Hujjaree, K., and Wang, X. (2021). Electroencephalographic Microstates in Schizophrenia and Bipolar Disorder. Front. Psychiatry, 12.","DOI":"10.3389\/fpsyt.2021.638722"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"84","DOI":"10.31276\/VJSTE.64(4).84-90","article-title":"Bipolar disorder traits: An electroencephalogram systematic review","volume":"64","author":"Nguyen","year":"2022","journal-title":"Minist. Sci. Technol. Vietnam"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.schres.2013.01.029","article-title":"Premorbid impairments in early-onset psychosis: Differences between patients with schizophrenia and bipolar disorder","volume":"146","author":"Otero","year":"2013","journal-title":"Schizophr. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107932","DOI":"10.1016\/j.patcog.2021.107932","article-title":"Adaptive, Hybrid Feature Selection (AHFS)","volume":"116","author":"Viharos","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nagy, A., Dombi, J., F\u00fclep, m.P., Rudics, E., Hompoth, E.A., Szab\u00f3, Z., D\u00e9r, A., B\u00faz\u00e1s, A., Viharos, Z.J., and Hoang, A.T. (2023). The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder. Sensors, 23.","DOI":"10.3390\/s23020958"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jain, A., Raja, R., Srivastava, S., Sharma, P.C., Gangrade, J., and Manoy, R. (2024). Analysis of EEG signals and data acquisition methods: A review. Comput. Methods Biomech. Biomed. Eng. Imaging Vis., 12.","DOI":"10.1080\/21681163.2024.2304574"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"235","DOI":"10.18280\/ts.370209","article-title":"Automatic Detection of Schizophrenia by Applying Deep Learning over Spectrogram Images of EEG Signals","volume":"37","author":"Aslan","year":"2020","journal-title":"Trait. Du Signal"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102039","DOI":"10.1016\/j.artmed.2021.102039","article-title":"Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls","volume":"114","author":"Barros","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yasin, S., Hussain, S.A., Aslan, S., Raza, I., Muzammel, M., and Othmani, A. (2021). EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review. Comput. Methods Programs Biomed., 202.","DOI":"10.1016\/j.cmpb.2021.106007"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.clinph.2022.11.014","article-title":"Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data","volume":"146","author":"Ravan","year":"2023","journal-title":"Clin. Neurophysiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102158","DOI":"10.1016\/j.artmed.2021.102158","article-title":"The three ghosts of medical AI: Can the black-box present deliver?","volume":"124","author":"Quinn","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s13246-015-0375-0","article-title":"EEG classification of adolescents with type I and type II of bipolar disorder","volume":"38","author":"Khaleghi","year":"2015","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lei, Y., Belkacem, A.N., Wang, X., Sha, S., Wang, C., and Chen, C. (2022). A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders. Biomed. Signal Process. Control, 72.","DOI":"10.1016\/j.bspc.2021.103370"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s13246-021-01038-7","article-title":"Automatic classification of schizophrenia patients using resting-state EEG signals","volume":"44","author":"Najafzadeh","year":"2021","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.inffus.2022.12.019","article-title":"Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques","volume":"92","author":"Hassan","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"de Miras, J.R., Ib\u00e1\u00f1ez-Molina, A.J., Soriano, m.F., and Iglesias-Parro, S. (2023). Schizophrenia classification using machine learning on resting state EEG signal. Biomed. Signal Process. Control, 79.","DOI":"10.1016\/j.bspc.2022.104233"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.neubiorev.2018.04.006","article-title":"Gamma band oscillations in the early phase of psychosis: A systematic review","volume":"90","author":"Reilly","year":"2018","journal-title":"Neurosci. \u2018I\u2019 Biobehav. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.psychres.2014.12.044","article-title":"Neurocognitive functioning in the premorbid stage and in the first episode of bipolar disorder: A systematic review","volume":"226","author":"Martino","year":"2015","journal-title":"Psychiatry Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.psychres.2018.12.169","article-title":"Premorbid adjustment trajectories in schizophrenia and bipolar disorder: A transdiagnostic cluster analysis","volume":"272","author":"Chan","year":"2019","journal-title":"Psychiatry Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"100246","DOI":"10.1016\/j.scog.2022.100246","article-title":"A systematic review of premorbid cognitive functioning and its timing of onset in schizophrenia spectrum disorders","volume":"28","author":"Mohn","year":"2022","journal-title":"Schizophr. Res. Cogn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jad.2004.12.001","article-title":"TEMPS-A: Progress towards validation of a self-rated clinical version of the Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire","volume":"85","author":"Akiskal","year":"2005","journal-title":"J. Affect. Disord."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.paid.2015.10.039","article-title":"Factorial structure of the Hungarian version of Oxford-Liverpool Inventory of Feelings and Experiences and its applicability on the schizophrenia-schizotypy continuum","volume":"90","author":"Nemes","year":"2016","journal-title":"Personal. Individ. Differ."},{"key":"ref_42","unstructured":"First, M.B. (2014). Structured clinical interview for the DSM (SCID). The Encyclopedia of Clinical Psychology, John Wiley & Sons."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1093\/oxfordjournals.schbul.a033401","article-title":"Measurement of Delusional Ideation in the Normal Population: Introducing the PDI (Peters et al. Delusions Inventory)","volume":"25","author":"Peters","year":"1999","journal-title":"Schizophr. Bull."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1176\/appi.ajp.157.11.1873","article-title":"Development and Validation of a Screening Instrument for Bipolar Spectrum Disorder: The Mood Disorder Questionnaire","volume":"157","author":"Hirschfeld","year":"2000","journal-title":"Am. J. Psychiatry"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1159\/000088441","article-title":"EASE: Examination of Anomalous Self-Experience","volume":"38","author":"Parnas","year":"2005","journal-title":"Psychopathology"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cloninger, C., Przybeck, T., Svrakic, D., and Wetzel, R. (1994). The Temperament and Character Inventory (TCI): A Guide to Its Development and Use: Center for Psychobiology of Personality, Washington University.","DOI":"10.1037\/t03902-000"},{"key":"ref_47","first-page":"97","article-title":"A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms","volume":"4","author":"Horne","year":"1976","journal-title":"Int. J. Chronobiol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1556\/0016.2016.71.4.1","article-title":"A viselked\u00e9ses g\u00e1tl\u00f3 \u00e9s aktiv\u00e1l\u00f3 rendszer sk\u00e1l\u00e1k (BIS-BAS) hazai adapt\u00e1ci\u00f3ja","volume":"71","author":"Hargitai","year":"2016","journal-title":"Magy. Pszichol\u00f3giai Szle."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1017\/S1092852917000578","article-title":"Is there enough \u201cinterest in and pleasure in\u201d the concept of depression? The development of the Leuven Affect and Pleasure Scale (LAPS)","volume":"24","author":"Demyttenaere","year":"2017","journal-title":"CNS Spectrums"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1111\/j.2044-8341.1941.tb00316.x","article-title":"Standardization of progressive matrices, 1938","volume":"19","author":"Raven","year":"1941","journal-title":"Br. J. Med. Psychol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"873","DOI":"10.4088\/JCP.16m11329","article-title":"The THINC-Integrated Tool (THINC-it) Screening Assessment for Cognitive Dysfunction: Validation in Patients With Major Depressive Disorder","volume":"78","author":"McIntyre","year":"2017","journal-title":"J. Clin. Psychiatry"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gramfort, A., Luessi, M., Larson, E., Engemann, D.A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., and Parkkonen, L. (2013). MEG and EEG data analysis with MNE-Python. Front. Neurosci., 7.","DOI":"10.3389\/fnins.2013.00267"},{"key":"ref_53","unstructured":"Lichman, M. (2013). UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.bspc.2015.04.002","article-title":"A permutation Lempel-Ziv complexity measure for EEG analysis","volume":"19","author":"Bai","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.clinph.2014.07.012","article-title":"Multiscale Lempel\u2013Ziv complexity for EEG measures","volume":"126","author":"Soriano","year":"2015","journal-title":"Clin. Neurophysiol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5047","DOI":"10.1111\/ejn.15800","article-title":"Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations","volume":"56","author":"Lau","year":"2022","journal-title":"Eur. J. Neurosci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.clinph.2008.01.104","article-title":"Abnormal EEG complexity in patients with schizophrenia and depression","volume":"119","author":"Li","year":"2008","journal-title":"Clin. Neurophysiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.pnpbp.2012.03.015","article-title":"Complexity and schizophrenia","volume":"45","author":"Hornero","year":"2013","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref_59","first-page":"493","article-title":"Brain Complexity and Psychiatric Dirsorders","volume":"18","author":"Ugaz","year":"2023","journal-title":"Iran. J. Psychiatry"},{"key":"ref_60","first-page":"1","article-title":"Visualization of EEG signal entropy in schizophrenia","volume":"12","author":"Kutepov","year":"2020","journal-title":"Sci. Vis."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Prabhu, S.K., and Martis, R.J. (2020, January 2\u20134). Diagnosis of Schizophrenia using Kolmogorov Complexity and Sample Entropy. Proceedings of the IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT50063.2020.9198472"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ansari, N., Khan, Y., and Farooq, O. (2022, January 26\u201327). A simple and efficient automated diagnostic model for Schizophrenia detection based on Hjorth Complexity. Proceedings of the 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), Aligarh, India.","DOI":"10.1109\/IMPACT55510.2022.10029132"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1038\/nature09552","article-title":"Rethinking schizophrenia","volume":"468","author":"Insel","year":"2010","journal-title":"Nature"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/mp.2014.71","article-title":"All the world\u2019s a (clinical) stage: Rethinking bipolar disorder from a longitudinal perspective","volume":"20","author":"Frank","year":"2014","journal-title":"Mol. Psychiatry"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s00062-009-9002-3","article-title":"One Century of Brain Mapping Using Brodmann Areas","volume":"19","author":"Strotzer","year":"2009","journal-title":"Clin. Neuroradiol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e2476","DOI":"10.1002\/brb3.2476","article-title":"Variability of EEG electrode positions and their underlying brain regions: Visualizing gel artifacts from a simultaneous EEG-fMRI dataset","volume":"12","author":"Scrivener","year":"2022","journal-title":"Brain Behav."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1038\/s41583-022-00583-8","article-title":"Causal mapping of human brain function","volume":"23","author":"Siddiqi","year":"2022","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.schres.2009.03.005","article-title":"Schizophrenia, \u201cjust the facts\u201d 4. Clinical features and conceptualization","volume":"110","author":"Tandon","year":"2009","journal-title":"Schizophr. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/S0165-0173(03)00193-0","article-title":"Studying spontaneous EEG activity with fMRI","volume":"43","author":"Friston","year":"2003","journal-title":"Brain Res. Rev."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Neuner, I., Arrubla, J., Werner, C.J., Hitz, K., Boers, F., Kawohl, W., and Shah, N.J. (2014). The Default Mode Network and EEG Regional Spectral Power: A Simultaneous fMRI-EEG Study. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0088214"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Daly, I., Williams, D., Hwang, F., Kirke, A., Miranda, E.R., and Nasuto, S.J. (2019). Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-45105-2"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s10548-020-00805-1","article-title":"EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States","volume":"34","author":"Abreu","year":"2020","journal-title":"Brain Topogr."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Choi, K.M., Kim, J.Y., Kim, Y.W., Han, J.W., Im, C.H., and Lee, S.H. (2021). Comparative analysis of default mode networks in major psychiatric disorders using resting-state EEG. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-03736-4"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"117864","DOI":"10.1016\/j.neuroimage.2021.117864","article-title":"The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T","volume":"231","author":"Wirsich","year":"2021","journal-title":"NeuroImage"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e2218949120","DOI":"10.1073\/pnas.2218949120","article-title":"Human brain effects of DMT assessed via EEG-fMRI","volume":"120","author":"Timmermann","year":"2023","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Warbrick, T. (2022). Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?. Sensors, 22.","DOI":"10.3390\/s22062262"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Macz\u00e1k, B., Vadai, G., D\u00e9r, A., Szendi, I., and Gingl, Z. (2021). Detailed analysis and comparison of different activity metrics. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0261718"},{"key":"ref_78","unstructured":"Gasso, G. (2019). Logistic Regression, INSA Rouen-ASI Departement Laboratory."},{"key":"ref_79","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/4\/454\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:32:41Z","timestamp":1760027561000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/4\/454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,13]]},"references-count":79,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["diagnostics15040454"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15040454","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,13]]}}}